@inproceedings{ed1770347c704c9f9436a0a40dc99df7,
title = "Nonlinear unmixing of hyperspectral images based on multi-kernel learning",
abstract = "Nonlinear unmixing of hyperspectral images has generated considerable interest among researchers, as it may overcome some inherent limitations of the linear mixing model. In this paper, we formulate the problem of estimating abundances of a nonlinear mixture of hyperspectral data based on a new multi-kernel learning paradigm. Experiments are conducted using both synthetic and real images in order to illustrate the effectiveness of the proposed method.",
keywords = "Hyperspectral image, multi-kernel learning, nonlinear unmixing",
author = "Jie Chen and C{\'e}dric Richard and Paul Honeine",
year = "2012",
doi = "10.1109/WHISPERS.2012.6874231",
language = "英语",
isbn = "9781479934065",
series = "Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing",
publisher = "IEEE Computer Society",
booktitle = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012",
note = "2012 4th Workshop on Hyperspectral Image and Signal Processing, WHISPERS 2012 ; Conference date: 04-06-2012 Through 07-06-2012",
}